Germany: LK Ostallgäu (Bayern)¶

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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
Notebook executed on: 07/03/2023 11:12:56 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ostallgäu", weeks=5);
2023-03-07T11:13:09.490896 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 87.3 LK Ostallgäu, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Ostallgäu new deaths (rolling 7d mean) Germany-LK Ostallgäu new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Ostallgäu cases daily growth factor Germany-LK Ostallgäu cases daily growth factor (rolling mean) Germany-LK Ostallgäu estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Ostallgäu deaths daily growth factor Germany-LK Ostallgäu deaths daily growth factor (rolling mean) Germany-LK Ostallgäu estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Ostallgäu doubling time cases (rolling mean) 0.00 14.32 28.65 42.97 57.29 daily change Germany-LK Ostallgäu new cases (rolling 7d mean) Germany-LK Ostallgäu new cases 0.000 0.398 0.795
In [4]:
overview(country="Germany", subregion="LK Ostallgäu");
2023-03-07T11:13:24.652079 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 7-day incidence rate (per 100K people) 87.3 LK Ostallgäu, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Ostallgäu cases daily growth factor Germany-LK Ostallgäu cases daily growth factor (rolling mean) Germany-LK Ostallgäu estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Ostallgäu deaths daily growth factor Germany-LK Ostallgäu deaths daily growth factor (rolling mean) Germany-LK Ostallgäu estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Ostallgäu doubling time cases (rolling mean) Germany-LK Ostallgäu doubling time deaths (rolling mean) 0.0 286.5 572.9 daily change Germany-LK Ostallgäu new cases (rolling 7d mean) Germany-LK Ostallgäu new cases 0.000 2.865 5.729 daily change Germany-LK Ostallgäu new deaths (rolling 7d mean) Germany-LK Ostallgäu new deaths 0.0 67.9 135.8 203.7 271.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ostallgäu", dates="2020-03-15:");
2023-03-07T11:16:14.867564 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: LK Ostallgäu LK Ostallgäu Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Ostallgäu Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ostallgäu")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ostallgäu")
print(f'Population of country="Germany", subregion="LK Ostallgäu": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table
Population of country="Germany", subregion="LK Ostallgäu": 143236 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 76464 30 313 0
2023-03-03 76434 3 313 0
2023-03-02 76431 17 313 0
2023-03-01 76414 38 313 0
2023-02-28 76376 37 313 0
2023-02-27 76339 69 313 0
2023-02-24 76270 22 313 0
2023-02-23 76248 35 313 0
2023-02-22 76213 38 313 0
2023-02-21 76175 22 313 0
2023-02-20 76153 45 313 0
2023-02-17 76108 21 313 0
2023-02-16 76087 24 313 0
2023-02-15 76063 36 313 0
2023-02-14 76027 44 313 0
2023-02-13 75983 54 313 0
2023-02-12 75929 1 313 0
2023-02-10 75928 12 313 0
2023-02-09 75916 33 313 0
2023-02-08 75883 23 313 0
2023-02-07 75860 47 313 0
2023-02-06 75813 47 313 0
2023-02-03 75766 15 313 0
2023-02-02 75751 23 313 0
2023-02-01 75728 24 313 0
2023-01-31 75704 30 313 0
2023-01-30 75674 34 313 0
2023-01-27 75640 16 313 0
2023-01-26 75624 12 313 0
2023-01-25 75612 9 313 0
2023-01-24 75603 26 313 0
2023-01-23 75577 21 313 1
2023-01-20 75556 15 312 0
2023-01-19 75541 10 312 0
2023-01-18 75531 12 312 0
2023-01-17 75519 19 312 0
2023-01-16 75500 23 312 0
2023-01-13 75477 16 312 0
2023-01-12 75461 17 312 0
2023-01-11 75444 29 312 0
2023-01-10 75415 36 312 0
2023-01-09 75379 37 312 0
2023-01-07 75342 1 312 0
2023-01-05 75341 24 312 0
2023-01-04 75317 15 312 0
2023-01-03 75302 24 312 0
2023-01-02 75278 44 312 2
2023-01-01 75234 1 310 0
2022-12-30 75233 12 310 0
2022-12-29 75221 19 310 0
2022-12-28 75202 37 310 0
2022-12-27 75165 61 310 1
2022-12-26 75104 1 309 0
2022-12-23 75103 21 309 0
2022-12-22 75082 30 309 0
2022-12-21 75052 19 309 0
2022-12-20 75033 35 309 0
2022-12-19 74998 47 309 0
2022-12-16 74951 13 309 0
2022-12-15 74938 43 309 0
2022-12-14 74895 18 309 0
2022-12-13 74877 27 309 0
2022-12-12 74850 48 309 0
2022-12-09 74802 16 309 0
2022-12-08 74786 29 309 0
2022-12-07 74757 38 309 0
2022-12-06 74719 45 309 0
2022-12-05 74674 49 309 0
2022-12-02 74625 15 309 0
2022-12-01 74610 29 309 0
2022-11-30 74581 28 309 0
2022-11-29 74553 29 309 0
2022-11-28 74524 40 309 0
2022-11-25 74484 17 309 0
2022-11-24 74467 25 309 1
2022-11-23 74442 13 308 0
2022-11-22 74429 39 308 0
2022-11-21 74390 35 308 0
2022-11-18 74355 11 308 0
2022-11-17 74344 20 308 0
2022-11-16 74324 29 308 0
2022-11-15 74295 45 308 0
2022-11-14 74250 44 308 1
2022-11-11 74206 22 307 0
2022-11-10 74184 31 307 1
2022-11-09 74153 42 306 1
2022-11-08 74111 45 305 0
2022-11-07 74066 59 305 0
2022-11-04 74007 64 305 0
2022-11-03 73943 51 305 1
2022-11-02 73892 101 304 0
2022-10-31 73791 81 304 1
2022-10-28 73710 57 303 0
2022-10-27 73653 81 303 3
2022-10-26 73572 81 300 0
2022-10-25 73491 185 300 0
2022-10-24 73306 194 300 2
2022-10-21 73112 96 298 0
2022-10-20 73016 199 298 1
2022-10-19 72817 186 297 0
2022-10-18 72631 274 297 0
2022-10-17 72357 399 297 1
2022-10-14 71958 176 296 0
2022-10-13 71782 253 296 0
2022-10-12 71529 396 296 0
2022-10-11 71133 332 296 0
2022-10-10 70801 467 296 2
2022-10-07 70334 251 294 0
2022-10-06 70083 640 294 1
2022-10-05 69443 400 293 1
2022-10-04 69043 496 292 1
2022-10-02 68547 2 291 0
2022-10-01 68545 1 291 0
2022-09-30 68544 156 291 0
2022-09-29 68388 220 291 1
2022-09-28 68168 252 290 0
2022-09-27 67916 350 290 1
2022-09-26 67566 213 289 0
2022-09-24 67353 1 289 0
2022-09-23 67352 55 289 1
2022-09-22 67297 131 288 0
2022-09-21 67166 108 288 0
2022-09-20 67058 93 288 0
2022-09-19 66965 116 288 0
2022-09-16 66849 33 288 0
2022-09-15 66816 39 288 0
2022-09-14 66777 64 288 0
2022-09-13 66713 66 288 0
2022-09-12 66647 70 288 0
2022-09-11 66577 1 288 0
2022-09-09 66576 17 288 0
2022-09-08 66559 61 288 0
2022-09-07 66498 59 288 0
2022-09-06 66439 71 288 0
2022-09-05 66368 64 288 0
2022-09-02 66304 35 288 0
2022-09-01 66269 55 288 0
2022-08-31 66214 56 288 0
2022-08-30 66158 64 288 0
2022-08-29 66094 68 288 0
2022-08-26 66026 13 288 0
2022-08-25 66013 54 288 0
2022-08-24 65959 52 288 0
2022-08-23 65907 71 288 0
2022-08-22 65836 70 288 0
2022-08-19 65766 28 288 0
2022-08-18 65738 80 288 0
2022-08-17 65658 120 288 0
2022-08-16 65538 133 288 1
2022-08-12 65405 46 287 0
2022-08-11 65359 56 287 0
2022-08-10 65303 146 287 0
2022-08-09 65157 134 287 0
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2022-08-05 64866 70 287 1
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Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)

In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")
Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")
Notebook execution took: 0:03:32.937902